Systems and methods for measuring radiated thermal energy during an additive manufacturing operation

ABSTRACT

This disclosure describes various methods and apparatus for characterizing an additive manufacturing process. A method for characterizing the additive manufacturing process can include generating scans of an energy source across a build plane; measuring an amount of energy radiated from the build plane during each of the scans using an optical sensing system that monitors two discrete wavelengths associated with a blackbody radiation curve of the layer of powder; determining temperature variations for an area of the build plane traversed by the scans based upon a ratio of sensor readings taken at the two discrete wavelengths; determining that the temperature variations are outside a threshold range of values; and thereafter, adjusting subsequent scans of the energy source across or proximate the area of the build plane.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 62/743,391, filed on Oct. 19, 2018, to U.S. Provisional PatentApplication No. 62/643,457 filed on Mar. 15, 2018 and to U.S.Provisional Patent Application No. 62/633,487, filed on Feb. 21, 2018,the disclosures of which are hereby incorporated by reference in theirentirety for all purposes.

BACKGROUND OF THE INVENTION

Additive manufacturing, or the sequential assembly or construction of apart through the combination of material addition and applied energy,takes on many forms and currently exists in many specificimplementations and embodiments. Additive manufacturing can be carriedout by using any of a number of various processes that involve theformation of a three dimensional part of virtually any shape. Thevarious processes have in common the sintering, curing or melting ofliquid, powdered or granular raw material, layer by layer usingultraviolet light, high powered laser, or electron beam, respectively.Unfortunately, established processes for determining a quality of aresulting part manufactured in this way are limited. Conventionalquality assurance testing generally involves post-process measurementsof mechanical, geometrical, or metallurgical properties of the part,which frequently results in destruction of the part. While destructivetesting is an accepted way of validating a part's quality, as it allowsfor close scrutiny of various internal features of the part, such testscannot for obvious reasons be applied to a production part.Consequently, ways of non-destructively and accurately verifying themechanical, geometrical and metallurgical properties of a productionpart produced by additive manufacturing are desired.

SUMMARY OF THE INVENTION

The described embodiments are related to additive manufacturing, whichinvolves using an energy source that takes the form of a moving regionof intense thermal energy. In the event that this thermal energy causesphysical melting of the added material, then these processes are knownbroadly as welding processes. In welding processes, the material, whichis incrementally and sequentially added, is melted by the energy sourcein a manner similar to a fusion weld. Exemplary welding processessuitable for use with the described embodiments include processes usinga scanning energy source with powder bed and wire-fed processes usingeither an arc, laser or electron beam as the energy source.

When the added material takes the form of layers of powder, after eachincremental layer of powder material is sequentially added to the partbeing constructed, the scanning energy source melts the incrementallyadded powder by welding regions of the powder layer creating a movingmolten region, hereinafter referred to as the melt pool, so that uponsolidification they become part of the previously sequentially added andmelted and solidified layers below the new layer to form the part beingconstructed. As additive machining processes can be lengthy and includeany number of passes of the melt pool, it can be difficult to avoid atleast slight variations in the size and temperature of the melt pool asthe melt pool is used to solidify the part. Embodiments described hereinreduce or minimize discontinuities caused by the variations in size andtemperature of the melt pool. It should be noted that additivemanufacturing processes can be driven by one or more processorsassociated with a computer numerical control (CNC) due to the high ratesof travel of the heating element and complex patterns needed to form athree dimensional structure.

An overall object of the described embodiments is to apply opticalsensing techniques for example, quality inference, process control, orboth, to additive manufacturing processes. Optical sensors can be usedto track the evolution of in-process physical phenomena by tracking theevolution of their associated in-process physical variables. Hereinoptical can include that portion of the electromagnetic spectrum thatincludes near infrared (IR), visible, and well as near ultraviolet (UV).Generally the optical spectrum is considered to go from 380 nm to 780 nmin terms of wavelength. However near UV and IR could extend as low as 1nm and as high as 3000 nm in terms of wavelength respectively. Sensorreadings collected from optical sensors can be used to determine inprocess quality metrics (IPQMs). One such IPQM is thermal energy density(TED), which is helpful in characterizing the amount of energy appliedto different regions of the part.

TED is a metric that is sensitive to user-defined laser powder bedfusion process parameters, for example, laser power, laser speed, hatchspacing, etc. This metric can then be used for analysis using IPQMcomparison to a baseline dataset. The resulting IPQM can be calculatedfor every scan and displayed in a graph or in three dimensions using apoint-cloud. Also, IPQM comparisons to the baseline dataset indicativeof manufacturing defects may be used to generate control signals forprocess parameters. In some embodiments, where detailed thermal analysisis desired, thermal energy density can be determined for discreteportions of each scan. In some embodiments, thermal energy data frommultiple scans can be divided into discrete grid regions of a grid,allowing each grid region to reflect a total amount of energy receivedat each grid region for a layer or a predefined number of layers.

An additive manufacturing method is disclosed and includes thefollowing: identifying spectral peaks associated with a batch of powder;selecting a first wavelength and a second wavelength spaced apart fromthe first wavelength, the first and second wavelengths being offset fromthe identified spectral peaks; generating a plurality of scans of anenergy source across a layer of the batch of powder disposed upon abuild plane during an additive manufacturing operation; measuring anamount of energy radiated from the build plane at the first wavelength;measuring an amount of energy radiated from the build plane at thesecond wavelength; determining variations in temperature of an area ofthe build plane traversed by the plurality of scans based upon a ratioof energy radiated at the first wavelength to energy radiated at thesecond wavelength; determining that the variations in temperature areoutside a threshold range of values; and thereafter, adjustingsubsequent scans of the energy source across or proximate the area ofthe build plane.

An additive manufacturing method is disclosed and includes thefollowing: identifying spectral peaks associated with a batch of powder;selecting a first wavelength and a second wavelength spaced apart fromthe first wavelength, the first and second wavelengths being offset fromthe identified spectral peaks; generating a plurality of scans of anenergy source across a layer of the batch of powder on a build plane;generating sensor readings during each of the plurality of scans usingan optical sensing system that monitors the first wavelength and thesecond wavelength; determining variations in temperature across thebuild plane during the plurality of scans using a ratio of the sensorreadings collected at the first wavelength to the sensor readingscollected at the second wavelength; determining when the variations intemperature are outside a threshold range of values; and thereafter,adjusting an output of the energy source.

An additive manufacturing method is disclosed and includes thefollowing: identifying spectral peaks associated with a batch of powder;selecting a first wavelength and a second wavelength spaced apart fromthe first wavelength, the first and second wavelengths being offset fromthe identified spectral peaks; generating a plurality of scans of anenergy source across a layer of powder on a build plane; generatingsensor readings during each of the plurality of scans using an opticalsensing system that monitors the first and second wavelengths during theplurality of scans; for each of the plurality of scans, mapping portionsof each of the sensor readings to a respective one of a plurality ofregions of the build plane; for each of the plurality of regions:characterizing temperature variations within the region based on a ratioof the sensor readings taken at the first wavelength and the sensorreadings taken at the second wavelength; determining that thetemperature variations associated with one or more of the plurality ofregions are outside a threshold range of values; and thereafter,adjusting an output of the energy source.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure will be readily understood by the following detaileddescription in conjunction with the accompanying drawings, wherein likereference numerals designate like structural elements, and in which:

FIG. 1A is a schematic illustration of an optical sensing apparatus usedin an additive manufacturing system with an energy source, in thisspecific instance taken to be a laser beam;

FIG. 1B is a schematic illustration of an optical sensing apparatus usedin an additive manufacturing system with an energy source, in thisspecific instance taken to be an electron beam;

FIG. 2 shows sample scan patterns used in additive manufacturingprocesses;

FIG. 3 shows a flow chart representing a method for identifying portionsof the part most likely to contain manufacturing defects;

FIGS. 4A-4H show the data associated with the step by step process toidentify a portion of the part most likely to contain a manufacturingdefect using the thermal energy density;

FIG. 5 shows a flow chart describing in detail how to use scanlet datasegregation to complete an IPQM assessment;

FIGS. 6A-6F show the data associated with the step by step process toidentify a portion of the part most likely to contain a manufacturingdefect using the thermal energy density; and

FIGS. 7A-7C show test results comparing IPQM metrics to post processmetallography;

FIG. 8 shows an alternative process in which data recorded by an opticalsensor such as a non-imaging photodetector can be processed tocharacterize an additive manufacturing build process;

FIGS. 9A-9D show visual depictions indicating how multiple scans cancontribute to the power introduced at individual grid regions;

FIG. 10A shows an exemplary turbine blade suitable for use with thedescribed embodiments;

FIG. 10B shows an exemplary manufacturing configuration in which 25turbine blades can be concurrently manufactured atop a build plane 1006;

FIGS. 10C-10D show different cross-sectional views of different layersof the configuration depicted in FIG. 10B;

FIGS. 11A-11B show cross-sectional views of base portions of twodifferent turbine blades;

FIG. 11C shows a picture illustrating the difference in surfaceconsistency between two different base portions;

FIG. 12 illustrates thermal energy density for parts associated withmultiple different builds;

FIGS. 13-14B illustrate an example of how thermal energy density can beused to control operation of a part using in-situ measurements;

FIG. 14C shows another power-density graph emphasizing various physicaleffects resulting from energy source settings falling too far out of aprocess window;

FIG. 14D shows how a size and shape of a melt pool can vary inaccordance with laser power and scanning velocity settings;

FIGS. 15A-15F illustrate how a grid can be dynamically created tocharacterize and control an additive manufacturing operation;

FIG. 16 shows an exemplary control loop 1600 for establishing andmaintaining feedback control of an additive manufacturing operation;

FIG. 17A shows a normal distribution of powder across a build plate;

FIG. 17B shows how when an insufficient amount of powder is retrievedand spread across the build plate by a recoater arm, a thickness of aresulting layer of powder can vary;

FIG. 17C shows a black and white photo of a build plate in which a shortfeed of powder resulted in only partial coverage of nine workpiecesarranged on a build plate;

FIG. 17D shows how when an energy source scans across all nineworkpieces using the same input parameters, detected thermal energydensity is substantially different;

FIG. 18A shows an exemplary graph illustrating sensor readings taken bya spectrometer;

FIG. 18B shows an exemplary graph depicting at least a portion of sensorreadings taken by the spectrometer after placing a band pass filter onthe spectrometer;

FIG. 19A shows a graph illustrating a number of blackbody radiationcurves representative of various melt pool temperatures, ranging from3500K to 5500K;

FIG. 19B shows a graph and how a % change in power output effects thenatural log of the ratio of the intensities sensed at two discretewavelengths;

FIG. 20A shows an exemplary additive manufacturing system that isequipped with three optical sensors to characterize temperaturevariations as well as an amount of energy added to a build plane asdescribed above relative to FIGS. 18A-19B;

FIG. 20B shows a similar configuration to FIG. 20A with the exceptionthat its sensor assembly can be attached to the optics of a laser by afiber optic cable;

FIG. 21 shows a block diagram illustrating a method for measuringoptical emissions during an additive manufacturing process.

DETAILED DESCRIPTION

FIG. 1A shows an embodiment of an additive manufacturing system thatuses one or more optical sensing apparatus to determine the thermalenergy density. The thermal energy density is sensitive to changes inprocess parameters such as, for example, energy source power, energysource speed, and hatch spacing. The additive manufacturing system ofFIG. 1A uses a laser 100 as the energy source. The laser 100 emits alaser beam 101 which passes through a partially reflective mirror 102and enters a scanning and focusing system 103 which then projects thebeam to a small region 104 on the work platform 105. In someembodiments, the work platform is a powder bed. Optical energy 106 isemitted from the small region 104 on account of high materialtemperatures.

In some embodiments, the scanning and focusing system 103 can beconfigured to collect some of the optical energy 106 emitted from thebeam interaction region 104. The partially reflective mirror 102 canreflect the optical energy 106 as depicted by optical signal 107. Theoptical signal 107 may be interrogated by multiple on-axis opticalsensors 109 each receiving a portion of the optical signal 107 through aseries of additional partially reflective mirrors 108. It should benoted that in some embodiments, the additive manufacturing system couldonly include one on-axis optical sensor 109 with a fully reflectivemirror 108.

It should be noted that the collected optical signal 107 may not havethe same spectral content as the optical energy 106 emitted from thebeam interaction region 104 because the signal 107 has suffered someattenuation after going through multiple optical elements such aspartially reflective mirror 102, scanning and focusing system 103, andthe series of additional partially reflective mirrors 108. These opticalelements may each have their own transmission and absorptioncharacteristics resulting in varying amounts of attenuation that thuslimit certain portions of the spectrum of energy radiated from the beaminteraction region 104. The data generated by on-axis optical sensors109 may correspond to an amount of energy imparted on the work platform.

Examples of on-axis optical sensors 109 include but are not limited tophoto to electrical signal transducers (i.e. photodetectors) such aspyrometers and photodiodes. The optical sensors can also includespectrometers, and low or high speed cameras that operate in thevisible, ultraviolet, or the infrared frequency spectrum. The on-axisoptical sensors 109 are in a frame of reference which moves with thebeam, i.e., they see all regions that are touched by the laser beam andare able to collect optical signals 107 from all regions of the workplatform 105 touched as the laser beam 101 scans across work platform105. Because the optical energy 106 collected by the scanning andfocusing system 103 travels a path that is near parallel to the laserbeam, sensors 109 can be considered on-axis sensors.

In some embodiments, the additive manufacturing system can includeoff-axis sensors 110 that are in a stationary frame of reference withrespect to the laser beam 101. These off-axis sensors 110 will have agiven field of view 111 which could be very narrow or it could encompassthe entire work platform 105. Examples of these sensors could includebut are not limited to pyrometers, photodiodes, spectrometers, high orlow speed cameras operating in visible, ultraviolet, or IR spectralranges, etc. Off-axis sensors 110, not aligned with the energy source,are considered off-axis sensors. Off-axis sensors 110 could also besensors which combine a series of physical measurement modalities suchas a laser ultrasonic sensor which could actively excite or “ping” thedeposit with one laser beam and then use a laser interferometer tomeasure the resultant ultrasonic waves or “ringing” of the structure inorder to measure or predict mechanical properties or mechanicalintegrity of the deposit as it is being built. The laser ultrasonicsensor/interferometer system can be used to measure the elasticproperties of the material, which can provide insight into, for example,the porosity of the material and other materials properties.Additionally, defect formation that results in material vibration can bemeasured using the laser ultrasonic/sensor interferometer system.

Additionally, there could be contact sensors 113 on the mechanicaldevice, recoater arm 112, which spreads the powders. These sensors couldbe accelerometers, vibration sensors, etc. Lastly, there could be othertypes of sensors 114. These could include contact sensors such asthermocouples to measure macro thermal fields or could include acousticemission sensors which could detect cracking and other metallurgicalphenomena occurring in the deposit as it is being built. These contactsensors can be utilized during the powder addition process tocharacterize the operation of the recoater arm 112. Data collected bythe on-axis optical sensors 109 and the off-axis sensors 110 can be usedto detect process parameters associated with the recoater arm 112.Accordingly, non-uniformities in the surface of the spread powder can bedetected and addressed by the system. Rough surfaces resulting fromvariations in the powder spreading process can be characterized bycontact sensors 113 in order to anticipate possible problem areas ornon-uniformities in the resulting part.

In some embodiments, a peak in the powder spread can be fused by thelaser beam 101, resulting in the subsequent layer of powder having acorresponding peak. At some point, the peak could contact the recoaterarm 112, potentially damaging the recoater arm 112 and resulting inadditional spread powder non-uniformity. Accordingly, embodiments of thepresent invention can detect the non-uniformities in the spread powderbefore they result in non-uniformities in the build area on the workplatform 105. One of ordinary skill would recognize many variations,modifications, and alternatives.

In some embodiments, the on-axis optical sensors 109, off-axis sensors110, contact sensors 113, and other sensors 114 can be configured togenerate in-process raw sensor data. In other embodiments, the on-axisoptical sensors 109, off-axis optical sensors 110, contact sensors 113,and other sensors 114 can be configured to process the data and generatereduced order sensor data.

In some embodiments, a computer 116, including a processor 118, computerreadable medium 120, and an I/O interface 122, is provided and coupledto suitable system components of the additive manufacturing system inorder to collect data from the various sensors. Data received by thecomputer 116 can include in-process raw sensor data and/or reduced ordersensor data. The processor 118 can use in-process raw sensor data and/orreduced order sensor data to determine laser 100 power and controlinformation, including coordinates in relation to the work platform 105.In other embodiments, the computer 116, including the processor 118,computer readable medium 120, and an I/O interface 122, can provide forcontrol of the various system components. The computer 116 can send,receive, and monitor control information associated with the laser 100,the work platform 105, and the recoater arm 112 in order to control andadjust the respective process parameters for each component.

The processor 118 can be used to perform calculations using the datacollected by the various sensors to generate in process quality metrics.In some embodiments, data generated by on-axis optical sensors 109,and/or the off-axis sensors 110 can be used to determine the thermalenergy density during the build process. Control information associatedwith movement of the energy source across the build plane can bereceived by the processor. The processor can then use the controlinformation to correlate data from on-axis optical sensor(s) 109 and/oroff-axis optical sensor(s) 110 with a corresponding location. Thiscorrelated data can then be combined to calculate thermal energydensity. In some embodiments, the thermal energy density and/or othermetrics can be used by the processor 118 to generate control signals forprocess parameters, for example, laser power, laser speed, hatchspacing, and other process parameters in response to the thermal energydensity or other metrics falling outside of desired ranges. In this way,a problem that might otherwise ruin a production part can beameliorated. In embodiments where multiple parts are being generated atonce, prompt corrections to the process parameters in response tometrics falling outside desired ranges can prevent adjacent parts fromreceiving too much or too little energy from the energy source.

In some embodiments, the I/O interface 122 can be configured to transmitdata collected to a remote location. The I/O interface can be configuredto receive data from a remote location. The data received can includebaseline datasets, historical data, post-process inspection data, andclassifier data. The remote computing system can calculate in-processquality metrics using the data transmitted by the additive manufacturingsystem. The remote computing system can transmit information to the I/Ointerface 122 in response to particular in-process quality metrics.

In the case of an electron beam system, FIG. 1B shows possibleconfigurations and arrangements of sensors. The electron beam gun 150generates an electron beam 151 that is focused by the electromagneticfocusing system 152 and is then deflected by the electromagneticdeflection system 153 resulting in a finely focused and targetedelectron beam 154. The electron beam 154 creates a hot beam-materialinteraction zone 155 on the workpiece 156. Optical energy 158 isradiated from workpiece 156 which could be collected by a series ofoptical sensors 159, each with their own respective field of view 160which, again, could be locally isolated to the interaction region 155 orcould encompass the entire workpiece 156. Additionally, optical sensors159 could have their own tracking and scanning system which could followthe electron beam 154 as it moves across the workpiece 156.

Whether or not sensors 159 have optical tracking, the sensors 159 couldbe implemented as pyrometers, photodiodes, spectrometers, and high orlow speed cameras operating in the visible, UV, or IR spectral regions.The sensors 159 could also be sensors which combine a series of physicalmeasurement modalities such as a laser ultrasonic sensor which couldactively excite or “ping” the deposit with one laser beam and then use alaser interferometer to measure the resultant ultrasonic waves or“ringing” of the structure in order to measure or predict mechanicalproperties or mechanical integrity of the deposit as it is being built.Additionally, there could be contact sensors 113 on the recoater arm.These sensors could be accelerometers, vibration sensors, etc. Lastly,there could be other types of sensors 114. These could include contactsensors such as thermocouples to measure macro thermal fields or couldinclude acoustic emission sensors which could detect cracking and othermetallurgical phenomena occurring in the deposit as it is being built.In some embodiments, one or more thermocouples could be used tocalibrate temperature data gathered by sensors 159. It should be notedthat the sensors described in conjunction with FIGS. 1A and 1B can beused in the described ways to characterize performance of any additivemanufacturing process involving sequential material build up.

FIG. 2 illustrates possible hatch patterns for scanning an energy sourceacross a powder bed. In 200, a region of the workpiece is processed bythe energy source scanning along long path lengths that alternate indirection. In this embodiment, hatch spacing 204 is shown between afirst scan 206 and a second scan 208. In 202, a region of the workpieceis broken into smaller checkerboards 214 which can be scanned by a firstscan 210 and a second scan 212 sequentially left to right and top tobottom. In other embodiments, the scan order for the individualcheckerboards can be randomized. A number of hatch patterns can beutilized in conjunction with the additive manufacturing processdisclosed herein. One of ordinary skill in the art would recognize manyvariations, modifications, and alternatives.

FIG. 3 shows a flowchart that illustrates an exemplary process 300 thatuses data generated by an additive manufacturing system to determine athermal energy density and identify portions of a part most likely tocontain manufacturing defects. Data generated by the on-axis opticalsensors 109 and the off-axis optical sensors 110 can be used alone or incombination to determine the thermal energy density. At 302, a rawphotodiode data trace is received. The raw photodiode data trace can begenerated using, for example, voltage data generated by the sensor inresponse to detection of emitted thermal energy. At 304, a portion ofthe raw photodiode trace that corresponds to a particular scan,scan_(i). is identified. In some embodiments, the individual photodiodedata trace can be separated from the rest of the sensor readings byreferencing energy source drive signal data (drive signal responsiblefor maneuvering and actuating the energy source). At 306, determine thearea under the raw photodiode data trace for scan_(i), hereinafter,pdon_(i). In some embodiments, pdon_(i) can represent the integratedphotodiode voltage. In some embodiments, pdon_(i) represents the averagereading of the photodiode during scan_(i). At 308, identify the part, p,associated with scan_(i). The part identified at 308 can also have anassociated area of the part, A_(p). These two values can be determinedby correlating pdon_(i) with energy source location data as describedabove. The process can, at 310, calculate the total scan count. At 312 alength associated with scan_(i), L_(i) can be determined. L_(i) can becalculated using equation (1), where x1_(i), y1_(i) and x2_(i), y2_(i)represent respective beginning and end locations for scan_(i):

L _(i)=√{square root over ((x1_(i) −x2_(i))²+(y1_(i) −y2_(i))²)}  Eq(1)

At 314, the total length of all scans used to produce the part,Lsum_(p), can be determined. The Lsum_(p) over the part can bedetermined by summing the length of each scan, L_(i), associated withthe part. At 316, the prorated area of the scan, A_(i), can bedetermined. A_(i) can be calculated using equation (2):

$\begin{matrix}{A_{i} = \frac{\left( {A_{p}*L_{i}} \right)}{{Lsum}_{p}}} & {{Eq}(2)}\end{matrix}$

At 316, the prorated thermal energy density (TED) for the i^(th) scan,TED_(i), can be determined. TED_(i) is an example of a set of reducedorder process features. The TED is calculated using raw photodiode data.From this raw sensor data, the TED calculation extracts reduced orderprocess features from the raw sensor data. TED_(i) is sensitive to alluser defined laser powder bed fusion process parameters, for examplelaser power, laser speed, hatch spacing, and many more. TED_(i) can becalculated using equation (3):

$\begin{matrix}{{TED}_{i} = \frac{{pdon}_{i}}{A_{i}}} & {{Eq}(3)}\end{matrix}$

For the purposes of this discussion “reduced order” refers to one ormore of the following aspects: data compression, i.e., less data in thefeatures as compared to the raw data; data reduction, i.e. a systematicanalysis of the raw data which yields process metrics or other figuresof merit; data aggregation, i.e. the clustering of data into discretegroupings and a smaller set of variables that characterize theclustering as opposed to the raw data itself; data transformation, i.e.the mathematical manipulation of data to linearly or non-linearly mapthe raw data into another variable space of lower dimensionality using atransformation law or algorithm; or any other related such techniqueswhich will have the net effect of reducing data density, reducing datadimensionality, reducing data size, transforming data into anotherreduced space, or all of these either effected simultaneously.

TED_(i) can be used for analysis during in process quality metric (IPQM)comparison to a baseline dataset. A resulting IPQM can be calculated forevery scan. At 318, the IPQM quality baseline data set and thecalculated TED_(i) can be compared. In regions of the part where adifference between the calculated TED and baseline data set exceeds athreshold value, those regions can be identified as possibly includingone or more defects and/or further processing can be performed on theregion in near real-time to ameliorate any defects caused by thevariation of TED from the baseline data set. In some embodiments, theportions of the part that may contain defects can be identified using aclassifier. The classifier is capable of grouping the results as beingeither nominal or off-nominal and could be represented through graphicaland/or text-based mediums. The classifier could use multipleclassification methods including, but not limited to: statisticalclassification, both single and multivariable; heuristic basedclassifiers; expert system based classifiers; lookup table basedclassifiers; classifiers based simply on upper or lower control limits;classifiers which work in conjunction with one or more statisticaldistributions which could establish nominal versus off-nominalthresholds based on confidence intervals and/or a consideration of thedegrees of freedom; or any other classification scheme whether implicitor explicit which is capable of discerning whether a set of feature datais nominal or off-nominal. For the purposes of this discussion,“nominal” will mean a set of process outcomes which were within apre-defined specification, which result in post-process measuredattributes of the parts thus manufactured falling within a regime ofvalues which are deemed acceptable, or any other quantitative,semi-quantitative, objective, or subjective methodology for establishingan “acceptable” component. Additional description related toclassification of IPQMs is provided in U.S. patent application Ser. No.15/282,822, filed on Sep. 30, 2016, the disclosure of which is herebyincorporated by reference in its entirety for all purposes.

It should be appreciated that the specific steps illustrated in FIG. 3provide a particular method of collecting data and determining thethermal energy density according to an embodiment of the presentinvention. Other sequences of steps may also be performed according toalternative embodiments. Moreover, the individual steps illustrated inFIG. 3 may include multiple substeps that may be performed in varioussequences as appropriate to the individual step. Furthermore, additionalsteps may be added or existing steps may be removed depending on theparticular applications. One of ordinary skill in the art wouldrecognize many variations, modifications, and alternatives.

FIGS. 4A-4H illustrate the steps used in the process 300 to determinethe TED and identify any portions of the part likely to contain defects.FIG. 4A corresponds to step 302 and shows a raw photodiode signal 402for a given scan length. The x-axis 450 indicates time in seconds andthe y-axis 460 indicates the photodiode voltage. In some embodiments,optical measurements could instead, or in addition to, be made by apyrometer. The signal 402 is the photodiode raw voltage. The rise 404and fall 406 of the photodiode signal 402 can be clearly seen as well asthe scatter and variation 408 in the signal during the time that thelaser is on. The data is collected at a given number of samples persecond. The variation 408 in photodiode signal 402 can be caused byvariations in powder being melted on the powder bed. For example, one ofthe minor troughs of photodiode signal 402 can be caused by energy beingabsorbed by a larger particle in the particle bed transitioning from asolid state to a liquid state. In general, the number of data points ina given segment of the photodiode signal between rise and fall eventscan be related to the scan duration and the sampling rate.

FIG. 4B shows a raw photodiode signal 402 and a laser drive signal 410.The laser drive signal 410 depicted in FIG. 4B can be produced usingenergy source drive signal data, in this case the laser drive signal410, or a command signal which tells the laser to turn on and off for aspecific scan length. The photodiode signal 402 is superimposed over thelaser drive signal 410. The rise 412 and fall 414 of the laser drivesignal 410 correspond to the rise 404 and fall 406 of the photodiodesignal 402. The data illustrated in FIG. 4B can be used to at step 304to identify a portion of the raw photodiode signal 402 that correspondsto a scan. In some embodiments, the laser drive signal 410 is ˜0V whenthe laser is off and ˜5 V when the laser is on. Step 304 can beaccomplished by isolating all the data associated with the photodiodesignal where the laser drive signal 410 is above a certain threshold,for example, 4.5V, and exclude all data where the laser is below thisthreshold from analysis.

FIG. 4C shows one embodiment of step 306, which includes determining thearea 416 under the raw photodiode signal 402. In some embodiments, thearea under the curve can be calculated using equation (4):

pdon _(i)=∫_(rise) ^(fall) V(t)dt  Eq (4)

The integrated photodiode voltage 418 can be used to determine pdon_(i)for the TED_(i) calculation.

FIG. 4D shows a position of scan 420 relative to the part and the totalscan count 424. Both values can be used to determine a TED thatcorresponds to the scan location on the part. FIG. 4E shows the renderarea for a part of interest 426. FIG. 4E also shows a plurality ofadditional parts 428 and a witness coupon 430. All of the parts in FIG.4E are depicted positioned on a powder bed 432.

FIG. 4F shows a trace associated with a portion of the photodiode dataand the laser drive signal data corresponding to four scans that can beused with the rest of the photodiode data to determine the total samplecount 434. The total sample count can be used to calculate the totalscan length over the part, LSum_(p). The total sample count isdetermined by summing the laser-on time periods 436. In someembodiments, the total scan length can be determined using the sum ofthe laser on time periods and average speed of the scanning energysource during laser-on time periods.

After collecting the scan data, the TED for each layer can be calculatedfrom the TED associated with each laser scan and then displayed in agraph 440, shown in FIG. 4G. The graph 440 illustrates TED valuespositioned within nominal region 442 and off nominal region 444. The TEDregions are divided by baseline threshold 438. In this way, layers ofthe part likely to contain defects are easily identifiable. Furtheranalysis could then be focused on the layers with off-nominal TEDvalues.

FIG. 4H shows how the TED value for each scan can be displayed in threedimensions using a point-cloud 446. Point cloud 446 illustrates theposition in three-dimensional space of TED values from nominal region442 and off nominal region 444 by displaying off nominal values as adifferent color or intensity than nominal values. Off nominal values areindicative of portions of the part most likely to contain manufacturingdefects, such as, porosity from keyhole formation or voids resultingfrom a lack of fusion. In some embodiments, the system can generate andtransmit a control signal that will change one or more processparameters based on the TED.

FIG. 5 shows a flowchart that illustrates an exemplary process 500 thatuses data generated by an additive manufacturing system to determine athermal energy density and identify portions of a part most likely tocontain manufacturing defects. Data generated by the on-axis opticalsensors 109 and the off-axis optical sensors 110 can be used alone or incombination to determine the thermal energy density. At 502, photodiodetime series data can be collected. The photodiode time series data canbe generated using, for example, voltage data associated with thesensors. At 504, laser drive time series data is collected. The laserdrive time data may be associated with additional process parameterssuch as, laser power, laser speed, hatch spacing, x-y position, etc. Theprocess at 506 can slice the photodiode time series data by droppingportions of the photodiode time series data that correspond to portionsof the laser drive time series data that indicate a laser-off state. Insome embodiments, the laser drive signal is ˜0 V when the laser is offand ˜5 V when the laser is on. The process at 506 can isolate all thedata where the laser drive signal is above a certain threshold, forexample, 4.5 V, and exclude all data where the laser is below thisthreshold from analysis. In some embodiments, the photodiode signal thatdrops to ˜0.2 V periodically can be included in the sample series dataas these are times when the laser just turned on and the laser isheating the material.

The process at 506 outputs only the laser-on photodiode data 508. Thelaser on photodiode data can be used by the process at 510 to convertsthe time-series data into sample-series data. The process at 510segments the laser on photodiode data into ‘N’ sample sections. The useof 20 sample sections is meant to provide an example of one embodimentof the present invention. Any number of sample sections can be used withvarying degrees of accuracy/resolution. In some embodiments, the set ofsample sections can be referred to as a scanlet 520 since it generallytakes multiple scanlets 520 to make up a single scan. The process at 512can count the number of samples 516. The process at 514 can render anarea of the lased part. In some embodiments, the lased part area 518 canbe determined using the number of pixels in a display associated withthe lased part. In other embodiments, the area can be calculated usingthe number of scans and data associated with process parameters. At 522,a process normalizes the scanlet data using total sample count lasedpart area 518, and scanlet data 520. In the illustrated embodiment, thescanlet metric data 524 is the thermal energy density for portions ofthe part associated with each scanlet. In some embodiments, scan datacan also be broken down by scan type. For example, an additivemanufacturing machine can utilize scans having differentcharacteristics. In particular, contour scans, or those designed tofinish an outer surface of a part can have substantially more power thanscans designed to sinter interior regions of a part. For this reason,more consistent results can be obtained by also segregating the data byscan type. In some embodiments, identification of scan types can bebased on scan intensity, scan duration and/or scan location. In someembodiments, scan types can be identified by correlating the detectedscans with scans dictated by a scan plan associated with the part beingbuilt.

Next, a process 528 receives baseline scanlet metric data and thethermal energy density and outputs an IPQM quality assessment 530. TheIPQM quality assessment 530 can be used to identify portions of the partmost likely to contain manufacturing defects. The process 528 caninclude a classifier as discussed earlier in the specification. Inaddition to the methods and systems above, the process 528 can comparethe candidate data, for example the scanlet metric data 524 and thebaseline scanlet metric data using a Mahalanobis distance. In someembodiments, the Mahalanobis distance for each scanlet can be can becalculated using the baseline scanlet metric data. While the embodimentsdisclosed in relation to FIG. 5 discussed the use of a laser as anenergy source, it will be apparent to one of ordinary skill in the artthat many modifications and variations are possible in view of the aboveteachings, for example, the laser may be replaced with an electron beamor other suitable energy source.

It should be appreciated that the specific steps illustrated in FIG. 5provide a particular method of determining a thermal energy density andidentifying portions of a part most likely to contain manufacturingdefects according to another embodiment of the present invention. Othersequences of steps may also be performed according to alternativeembodiments. For example, alternative embodiments of the presentinvention may perform the steps outlined above in a different order.Moreover, the individual steps illustrated in FIG. 5 may includemultiple sub-steps that may be performed in various sequences asappropriate to the individual step. Furthermore, additional steps may beadded or removed depending on the particular applications. One ofordinary skill in the art would recognize many variations,modifications, and alternatives.

FIG. 6A shows photodiode time series data 602. The photo diode timeseries data can be collected from a variety of on-axis or off-axissensors as illustrated in FIGS. 1 and 2. The x-axis 604 indicates timein seconds and the y-axis 606 indicates voltage generated by the sensor.The voltage generated by the sensor is associated with energy emittedfrom the build plane that can impinge on one or more sensors. Thesamples 606 are illustrated on the trace of the photodiode time seriesdata 602. FIG. 4B describes the process at 506 where the photodiode datais associated with the laser drive signal.

FIG. 6B shows the laser on photodiode data. The x-axis represents thenumber of samples 608 and the y-axis 610 represents the voltage of theraw sensor data. The drops 612 in voltage are included in the analysisbecause, while the voltage is substantially lower, the laser is stillactively contributing to heating the material.

FIG. 6C shows the laser on photodiode sample series data discussed inrelation to step 510. The 20-sample sections 620 can have an arbitrarysize. Twenty samples corresponds to a laser travel distance of ˜400 μmwith a laser travel speed of 1000 mm/s. The noise in the XY signalitself is ˜150 μm. In some embodiments, with less than a 20-samplesection, for example, a 2 sample-section, the distance measured and thenoise would be in such a ratio that the location of a point could notconfidently be determined. In some embodiments, a limit of 50 samplescan be used for spatial resolution below 1 mm. Thus, the number ofsamples to segment the data into should be in the range of 20≤N≤50, for50 GHz data with a laser travel speed of 1000 mm/s. It will be apparentto one of ordinary skill in the art that many modifications andvariations are possible in view of the above teachings.

FIG. 6D corresponds to process 522 and illustrates an embodiment wherethe average value 618 of each scanlet is determined. In someembodiments, the inputs into process 522 include the total sample count516, the lased part area 518, and the scanlet data 520. Using theseinputs, the average can be used to determine the area under the curve(AUC) as illustrated in equation (5):

AUC=V(avg)*N(samples)  Eq(5)

Where V is the average voltage determined for each scanlet and N is thenumber of samples. In FIG. 6D, the average voltage of the 20-samplesegment is equivalent to integrating the signal because the width of thedata is fixed.

FIG. 6E shows the lased part area for an individual scanlet 622, A_(i),and for all scans 624. In addition to the area, the length of a scan,L_(i), and the sum of L_(i) over the entire part can be calculated,LSum_(p). L_(i) can be calculated using equation (6):

L _(i)=√{square root over ((x1_(i) −x2_(i))²+(y1_(i) −y2_(i))²)}  Eq(6)

The x and y coordinates for the beginning and end of the scan may beprovided or they may be determined based on one or more direct sensormeasurements.

FIG. 6F shows the render area 626 of a lased part associated with alayer in the build plane. In some embodiments, once pdon_(i), the areaof the part, A_(p), the length of the scan, L_(i), and the total lengthLSum_(p) are determined, TED can be calculated using equation (7):

$\begin{matrix}{{TED}_{i} = \frac{\left( {{pdon}_{i}*{LSum}_{p}} \right)}{\left( {A_{p}*L_{i}} \right)}} & {{Eq}(7)}\end{matrix}$

TED is sensitive to all user-defined laser powder bed fusion processparameters, for example, laser power, laser speed, hatch spacing, etc.The TED value can be used for analysis using an IPQM comparison to abaseline dataset. The resulting IPQM can be determined for every laserscan and displayed in a graph or in three dimensions using apoint-cloud. FIG. 4G shows an exemplary graph. FIG. 4H shows anexemplary point cloud.

FIGS. 7A-7C show post process porosity measurements and correspondingnormalized in-process TED measurements. The figures show that in-processTED measurements can be an accurate IPQM predictor of porosity and othermanufacturing defects. FIG. 7A shows the comparison of TED metric datato a baseline dataset. The plot shows the value of each photodiode inthe IPQM metric, both separate and combined. On-axis photodiode data 702can come from sensors aligned with the energy source. Off-axisphotodiode data 704 can be collected by sensors that are not alignedwith the energy source. The combination of on-axis and off-axisphotodiode data 706 yields the highest sensitivity to changes in processparameters. The x-axis 708 shows the build plane layer of the part; they-axis 710 shows the Mahalanobis distance between the calculated TED andthe baseline metric.

The Mahalanobis distance can be used to standardize the TED data. TheMahalanobis distance indicates how many standard deviations each TEDmeasurement is from a nominal distribution of TED measurements. In thiscase, the Mahalanobis Distance indicates how many standard deviationsaway each TED measurement is from the mean TED measurement collectedwhile building control layers 526-600. The chart below FIG. 7A alsoshows how TED varies with global energy density (GED) and porosity. Inparticular, for this set of experiments TED can be configured to predictpart porosity without the need to do destructive examination.

In some embodiments, the performance of the additive manufacturingdevice can be further verified by comparing quantitative metallographicfeatures (e.g. the size and shape of pores or intermetallic particles)and/or mechanical property features (e.g. strength, toughness orfatigue) of the metal parts created while performing the test runs. Ingeneral, the presence of unfused metal powder particles in the testparts indicates not enough energy was applied while test parts thatreceived too much energy tend to develop internal cavities that can bothcompromise the integrity of the created part. Porosity 714 can berepresentative of these defects.

In some embodiments, a nominal value used to generate FIG. 7A will betaken from a preceding test. In some embodiments, the nominal valuecould also be taken from a subsequent test since the calculations do notneed to be done during the additive manufacturing operation. Forexample, when attempting to compare performance of two additivemanufacturing devices, a nominal value can be identified by running atest using a first one of the additive manufacturing devices. Theperformance of the second additive manufacturing device could then becompared to the nominal values defined by the first additivemanufacturing device. In some embodiments, where performance of the twoadditive manufacturing devices is within a predetermined threshold offive standard deviations, comparable performance can be expected fromthe two machines. In some embodiments, the predetermined threshold canbe a 95% statistical confidence level derived from an inverse chisquared distribution. This type of test methodology can also be utilizedin identifying performance changes over time. For example, aftercalibrating a machine, results of a test pattern can be recorded. Aftera certain number of manufacturing operations are performed by thedevice, the additive manufacturing device can be operated again. Theinitial test pattern performed right after calibration can be used as abaseline to identify any changes in the performance of the additivemanufacturing device over time. In some embodiments, settings of theadditive manufacturing device can be adjusted to bring the additivemanufacturing device back to its post-calibration performance.

FIG. 7B shows the post process metallography for a part constructedusing an additive manufacturing process. FIG. 7B shows the part 718 anda corresponding cross-section 720 of the part. The sections 1 through 11correspond to sections 1 through 11 in FIG. 7A. The changes in processparameters, and the resulting changes in porosity, can be seen in thecross-section view 720 of the part. In particular, sections 2 and 3 havethe highest porosity, 3.38% and 1.62% respectively. The higher porosityis shown in the cross-section by the increased number of defect marks722 in the sample part.

FIG. 7C shows the IPQM results with the corresponding cross-sectiondetermined during metallography. Each cross-section includes the energydensity 724 in J/mm² and the porosity 714. The samples with the highestnumber of defect marks 722 correspond to the TED measurements with thehighest standardized distances from the baseline. The plot illustratesthat low standardized distance can be predictive of higher density andlow porosity metallography while a high standardized Mahalanobisdistance is highly correlated with high-porosity and poor metallography.For example, low power settings used in generating layers around layer200 result in a high porosity 714 and a large number of defect marks722. In comparison, using middle of the road settings on or around the600^(th) layer results in no identifiable defect marks 722 and thelowest recorded porosity value of 0.06%.

FIG. 8 shows an alternative process in which data recorded by an opticalsensor such as a non-imaging photodetector can be processed tocharacterize an additive manufacturing build process. At 802, raw sensordata is received that can include both build plane intensity data andenergy source drive signals correlated together. At 804, by comparingthe drive signal and build plane intensity data, individual scans can beidentified and located within the build plane. Generally the energysource drive signal will provide at least start and end positions fromwhich the area across which the scan extends can be determined. At 806,raw sensor data associated with an intensity or power of each scan canbe binned into corresponding X & Y grid regions. In some embodiments,the raw intensity or power data can be converted into energy units bycorrelating the dwell time of each scan in a particular grid region. Insome embodiments, each grid region can represent one pixel of an opticalsensor monitoring the build plane. It should be noted that differentcoordinate systems, such as polar coordinates, could be used to storegrid coordinates and that storage of coordinates should not be limitedto Cartesian coordinates. In some embodiments, different scan types canbe binned separately so that analysis can be performed solely onparticular scan types. For example, an operator may want to focus oncontour scans if those types of scans are most likely to includeunwanted variations. At 808, energy input at each grid region can besummed up so that a total amount of energy received at each grid regioncan be determined using equation (8).

E _(pd) _(m) =Σ_(n=1) ^(pixel samples in grid cell) E _(pd) _(n)   Eq(8)

This summation can be performed just prior to adding a new layer ofpowder to the build plane or alternatively, summation may be delayeduntil a predetermined number of layers of powder have been deposited.For example, summation could be performed only after having depositedand fused portions of five or ten different layers of powder during anadditive manufacturing process. In some embodiments, a sintered layer ofpowder can add about 40 microns to the thickness of a part; however thisthickness will vary depending on a type of powder being used and athickness of the powder layer.

At 810, the standard deviation for the samples detected and associatedwith each grid region is determined. This can help to identify gridregions where the power readings vary by a smaller or greater amount.Variations in standard deviation can be indicative of problems withsensor performance and/or instances where one or more scans are missingor having power level far outside of normal operating parameters.Standard deviation can be determined using Equation (9).

$\begin{matrix}{E_{{pd}_{sm}} = \sqrt{\frac{1}{{\# \mspace{14mu} {sample}\text{-}{in}\text{-}{location}} - 1}{\sum\limits_{n = 1}^{{sample}\text{-}{in}\text{-}{pixel}}\left( {E_{n} - \overset{\_}{E}} \right)^{2}}}} & {{Eq}(9)}\end{matrix}$

At 812, a total energy density received at each grid region can bedetermined by dividing the power readings by the overall area of thegrid region. In some embodiments, a grid region can have a squaregeometry with a length of about 250 microns. The energy density for eachgrid region can be determined using Equation (10).

$\begin{matrix}{E_{{grid}\mspace{14mu} {location}} = \frac{\sum\limits_{n = 1}^{{samples}\text{-}{in}\text{-}{location}}E_{{pd}_{n}}}{A_{{grid}\mspace{14mu} {location}}}} & {{Eq}(10)}\end{matrix}$

At 814, when more than one part is being built, different grid regionscan be associated with different parts. In some embodiments, a systemcan included stored part boundaries that can be used to quicklyassociate each grid region and its associated energy density with itsrespective part using the coordinates of the grid region and boundariesassociated with each part.

At 816, an area of each layer of a part can be determined. Where a layerincludes voids or helps define internal cavities, substantial portionsof the layer may not receive any energy. For this reason, the affectedarea can be calculated by summing only grid regions identified asreceiving some amount of energy from the energy source. At 818, thetotal amount of power received by the grid regions within the portion ofthe layer associated with the part can be summed up and divided by theaffected area to determine energy density for that layer of the part.Area and energy density can be calculated using Equations (11) and (12).

$\begin{matrix}{A_{part} = {\sum\limits_{n = 1}^{{part}\mspace{14mu} {pixel}}{1\left( {E_{{pd}_{n}} > 0} \right)}}} & {{Eq}(11)} \\{{IPQM}_{{part}_{layer}} = \frac{\sum\limits_{n = 1}^{{part}\mspace{14mu} {grid}\mspace{14mu} {locations}}E_{{pd}_{n}}}{A_{part}}} & {{Eq}(12)}\end{matrix}$

At 820, the energy density of each layer can be summed together toobtain a metric indicative of the overall amount of energy received bythe part. The overall energy density of the part can then be comparedwith the energy density of other similar parts on the build plane. At822, the total energy from each part is summed up. This allows highlevel comparisons to be made between different builds. Build comparisonscan be helpful in identifying systematic differences such as variationsin powder and changes in overall power output. Finally at 824, thesummed energy values can be compared with other layers, parts or buildplanes to determine a quality of the other layers, parts or buildplanes.

It should be appreciated that the specific steps illustrated in FIG. 8provide a particular method of characterizing an additive manufacturingbuild process according to another embodiment of the present invention.Other sequences of steps may also be performed according to alternativeembodiments. For example, alternative embodiments of the presentinvention may perform the steps outlined above in a different order.Moreover, the individual steps illustrated in FIG. 8 may includemultiple sub-steps that may be performed in various sequences asappropriate to the individual step. Furthermore, additional steps may beadded or removed depending on the particular applications. One ofordinary skill in the art would recognize many variations,modifications, and alternatives.

FIGS. 9A-9D show visual depictions indicating how multiple scans cancontribute to the power introduced at individual grid regions. FIG. 9Adepicts a grid pattern made up of multiple grid regions 902 distributedacross a portion of a part being built by an additive manufacturingsystem. In some embodiments, each individual grid region can have a sizeof between 100 and 500 microns; however it should be appreciated thatslightly smaller or larger grid regions are also possible. FIG. 9A alsodepicts a first pattern of energy scans 904 extending diagonally acrossa grid regions 902. The first pattern of energy scans 904 can be appliedby a laser or other intense source of thermal energy scanning acrossgrid regions 902. It should be noted that while energy scans aredepicted as having uniform energy density that in some embodiments, theenergy density of the scans can instead be modeled using a Gaussiandistribution. The Gaussian distribution can be used to more accuratelymodel the distribution of heat within each scan due to the heat beingmost highly concentrated at the point of incidence between the energysource (e.g. laser or electron beam) and a layer of powder on the buildplane and then becoming progressively less intense toward the edges ofthe scan. By more accurately modeling the beam, grid regions 902 on theedge of energy scans 904 are assigned a substantially smaller and moreaccurate amount of energy while grid regions falling within a centralportion of energy scans 904 are assigned a proportionately larger amountof energy.

FIG. 9B shows how the energy introduced across the part is representedin each of grid regions 902 by a singular gray scale colorrepresentative of an amount of energy received where darker shades ofgray correspond to greater amounts of energy. It should be noted that insome embodiments the size of grid regions 902 can be reduced to obtainhigher resolution data. Alternatively, the size of grid regions 902could be increased to reduce memory and processing power usage.

FIG. 9C shows a second pattern of energy scans 906 overlapping with atleast a portion of the energy scans of the first pattern of energyscans. As discussed in the text accompanying FIG. 8, where the first andsecond patterns of energy scans overlap, grid regions are shown in adarker shade to illustrate how energy from both scans has increased theamount of energy received over the overlapping scan patterns. Clearly,the method is not limited to two overlapping scans and could includemany other additional scans that would get added together to fullyrepresent energy received at each grid region.

FIG. 10A shows an exemplary turbine blade 1000 suitable for use with thedescribed embodiments. Turbine blade 1000 includes multiple differentsurfaces and includes a number of different features that require manydifferent types of complex scans to produce. In particular, turbineblade 1000 includes a hollow blade portion 1002 and a tapered baseportion 1004. FIG. 10B shows an exemplary manufacturing configuration inwhich 25 turbine blades 1000 can be concurrently manufactured atop buildplane 1006.

FIGS. 10C-10D show different cross-sectional views of different layersof the configuration depicted in FIG. 10B with grid TED basedvisualization layers. FIG. 10C shows layer 14 of turbine blades 1000 andthe TED based visualization layer illustrates how a lower end of selectones of base portion 1004 can define multiple voids in turbine blades1000-1, 1000-2 and 1000-3. Because energy density data is associatedwith discrete grid regions, these voids, which would otherwise becompletely concealed within the turbine blades are clearly visible inthis grid TED based visualization. FIG. 10D shows how an upper end ofbase portion 1004 can also define multiple concealed voids withinturbine blades 1000-1, 1000-2 and 1000-3, which are clearly discernablefrom the grid TED based visualization layer depicted in FIG. 10D.

FIGS. 11A-11B show cross-sectional views of base portions 1004-1 and1004-2 of two different turbine blades 1000. FIG. 11A shows base portion1004-1, which was produced using nominal manufacturing settings. Outersurfaces 1102 and 1104 of base portions 1004-1 receive substantiallymore energy than interior 1106 of base portion 1004-1. The increasedenergy input into the outer surfaces provides a more uniform hardenedsurface, resulting in an annealing effect being achieved along outersurfaces 1102 and 1104. This additional energy can be introduced usinghigher energy contour scans that target outer surfaces 1102 and 1104.FIG. 11B shows base portion 1004-2, which was produced with the samemanufacturing settings as 1004-2 with the exception of omitting thecontour scans. While all the scans utilized in the manufacturingoperation producing base portion 1004-2 were also included in producingbase portion 1004-1, summing energy density inputs for all the scanscovering each grid region allows an operator to clearly see a differencebetween base portions 1004-1 and 1004-2.

FIG. 11C shows the difference in surface consistency between baseportions 1004-1 and 1004-2. Clearly, omitting the contour scans for baseportion 1004-2 has a substantial effect on overall outer surfacequality. Outer surfaces for base portion 1004-1 are smoother and lessporous in consistency. The annealing effect on base portion 1004-1should also give it substantially more strength than base portion1004-2.

FIG. 12 illustrates thermal energy density for parts associated withmultiple different builds. Builds A-G each include thermal emission datafor about 50 different parts (represented by discrete circles 1202)built during the same additive manufacturing operation. Thisillustration shows how thermal emission data can be used to trackvariations between different builds. For example, lots A, B and C allhave similar TED distributions; however, builds D, E and F while stillbeing within tolerances have consistently lower thermal emission data.In some cases, these types of changes can be due to changes in powderlots. In this way, the thermal emission data can be used to tracksystematic errors that may negatively affect overall output quality. Itshould be noted that while this chart depicts mean TED values based ongrid TED that a similar chart could be constructed for a scan-based TEDmethodology.

FIGS. 13-14B illustrate an example of how thermal energy density can beused to control operation of a part using in-situ measurements. FIG. 13shows how parts being produced using different combinations of energysource power and scan speed can be produced and then destructivelyanalyzed to determine a resulting part density in grams/cubiccentimeter, as depicted. In this trial, a part associated with partdensity 1302, which has a part density of 4.37 g/cc, was produced usingmanufacturer recommended scan and laser power settings. Based on theresulting density readings, a position of dashed line 1304 can bedetermined. Line 1304 indicates where a resulting lowered amount ofenergy input results in portions of the powder not being sufficientlyheated to fuse together resulting in part densities that fall below athreshold density level. Part density can also be reduced when too muchenergy is added to the system resulting in the formation of keyholeswithin the parts resulting from powder being vaporized instead of meltedduring a manufacturing operation. Dashed line 1306 can be experimentallydetermined from the density data and in this example is identified bypart density falling as low as 4.33 g/cc. Line 1308 represents anoptimum energy density contour along which energy density and partgeometry stay substantially constant. The density testing shows how theaverage density of parts created using settings distributed along line1308 stays relatively consistent.

FIGS. 14A-14B show a thermal energy density contour overlaid upon aportion of the power-density graph and illustrate how thermal densitymeasurements collected during an additive manufacturing operation varydepending on the settings used by the energy source. As depicted inFIGS. 14A-14B darker shading indicates higher thermal energy densitiesand lighter shading indicates lower thermal energy densities. From thepart density testing and thermal energy density contours, control limitscan be determined for a particular part. In this case, the controllimits, indicated by ellipse 1402, have been determined and allow powerand scan speed parameters to vary from the settings used to produce part1302 by up to 3σ along line 1308 and 1σ along a line perpendicular toline 1308. In some embodiments, the allowable variation in power andspeed allows for in-process adjustments to be made in order to maintaina desired thermal energy density during production runs. Ellipse 1404indicates an overall process window that can accommodate furthervariations outside the control limits. In some embodiments, this processwindow can be used to identify variations that would still allow aresulting part to be validated using only in-process data. It should benoted that while the depicted control and process windows are shown aselliptical, other process window shapes and sizes are possible and maybe more complex depending on, for example, part geometries and materialeccentricities. During a manufacturing operation, optical sensorreadings measuring thermal radiation can be used to determine thermalenergy density in situ. In instances where thermal energy density fallsoutside an expected range when keeping the laser power and scanvelocities within the depicted control limits indicated by ellipse 1402,portions of the part with the abnormal thermal energy density readingscan be flagged as potential having defects.

In some embodiments, the process windows can be incorporated into amodeling and simulation program that models one or more optical sensorsthat collect sensor readings used to determine thermal energy density.Once the modeling and simulation system iterates to a firstapproximation of an instruction set for a workpiece, expected thermalenergy density can be output to an additive manufacturing machine forfurther testing. The modeled thermal energy density data can used by theadditive manufacturing machine for further testing and validation, whenthe additive manufacturing machine includes an optical sensor andcomputing equipment configured to measure thermal energy density. Acomparison of the modeled and measured thermal energy density can beused to confirm how closely performance of the instructions set matchesthe expected outcome in situ.

FIG. 14C shows another power-density graph emphasizing various physicaleffects resulting from energy source settings falling too far out ofprocess window 1404. For example, the power density graph shows thatadding large amounts of laser power at low scanning velocities resultsin keyholes being formed within the workpiece. Keyhole formation occursdue to portions of the powder material evaporating due to receiving toomuch energy. Furthermore, low power combined with high scanningvelocities can result in a failure of the powder to fuse together.Finally, high power levels combined with high scanning velocities resultin fused metal balling up during a build operation. It should be notedthat varying a thickness of the deposited power layer can result in ashift of the lines separating the conduction mode zone from keyholeformation zones and lack of fusion zones. For example, increasing thethickness of the powder layers has the effect of increasing the slope ofthe lines separating the keyhole and lack of fusion regions from theconduction mode zone as a thicker layer generally requires more energyto undergo liquefaction.

FIG. 14C also shows how power and scan velocity settings correspondingto the conduction mode zone don't generally result in any of theaforementioned serious defects; however, by keeping the laser settingscorresponding to values within process window 1404 a resulting grainstructure and/or density of the part can be optimized. Another benefitof keeping the energy source settings within process window 1404 is thatthese settings should keep thermal energy density readings within anarrow range of values. Any thermal energy density values fallingoutside of the predetermined range during a manufacturing operation canbe indicative of a problem in the manufacturing process. These problemscan be addressed by moving the settings closer to a central region ofthe process window and/or by updating the process window for subsequentparts. In some embodiments, a manufacturer could discover that the faultwas caused by defective powder or some other infrequent aberration thatshould not be factored into subsequent manufacturing processes. Itshould be appreciated that the depicted process window may not be thesame for all portions of a part and could vary greatly depending on whatregion or even specific layer of the part was being worked on at aparticular time. The size and/or shape of process window 1404 can alsovary in accordance with other factors such as hatch spacing, scan lengthand scan direction in some embodiments.

FIG. 14D shows how a size and shape of a melt pool can vary inaccordance with laser power and scanning velocity settings. Exemplarymelt pools 1406-1418 exhibit exemplary melt pool size and shape forvarious laser power and velocity settings. In general, it can be seenthat larger melt pools result from higher amounts of laser power andlower scan velocities. However, for this particular configuration meltpool size depends more upon laser power than velocity.

FIGS. 15A-15F illustrate how a grid can be dynamically created tocharacterize and control an additive manufacturing operation. FIG. 15Ashows a top view of a cylindrical workpiece 1502 positioned upon aportion of a build plane 1504. Workpiece 1502 is shown as it undergoesan additive manufacturing operation. FIG. 15B shows how cylindricalworkpiece 1502 can be divided into multiple tracks 1506 along which anenergy source can melt powder distributed on an upper surface ofcylindrical workpiece 1502. In some embodiments, the energy source canalternate directions 1506 as depicted while in other embodiments theenergy source can always move in one direction. Furthermore a directionof tracks 1506 can vary from layer to layer in order to furtherrandomize the orientation of scans used to build workpiece 1502.

FIG. 15C shows an exemplary scan pattern for the energy source as itforms a portion of workpiece 1502. As indicated, by arrow 1508 adirection of movement of across workpiece 1502 of an exemplary energysource is diagonal. Individual scans 1510 of the energy source can beoriented in a direction perpendicular to the direction of movement ofthe energy source along track 1506 and extend entirely across track1506. The energy source can turn off briefly between successiveindividual scans 1510. In some embodiments, a duty cycle of the energysource can be about 90% as it traverses each of tracks 1506. Byemploying this type of scan strategy, the energy source can cover awidth of track 1506 as it traverses across workpiece 1502. In someembodiments, swath 1510 can have a width of about 5 mm. This cansubstantially reduce the number of tracks needed to form workpiece 1502as in some embodiments a width of a melt pool generated by the energysource can be on the order of about 80 microns.

FIGS. 15D-15E show how grid regions 1512 can be dynamically generatedalong each track 1506 and be sized to accommodate a width of eachindividual scan 1510. A precise position of subsequent scans can beforecast by the system by referencing energy source drive signalsenroute to the energy source. In some embodiments the width of grids1512 can match the length of individual scans 1512 or be within 10 or20% of the length of individual scans 1512. Again, scan length ofindividual scans 1512 can be anticipated by referencing the energysource drive signals. In some embodiments, grid regions 1512 can besquare or rectangular in shape. A thermal energy density can bedetermined for each of grid regions 1512 as the energy source continuesalong track 1506. In some embodiments, thermal energy density readingswithin grid region 1512-1 could be used to adjust an output of theenergy source within the next grid region, grid region 1512-2 in thiscase. For example, if the thermal energy density readings generated byindividual scans 1510 within grid region 1512-1 are substantially higherthan expected, energy source power output can be reduced, a speed atwhich energy source scans across individual scans 1510 can be increasedand/or spacing between individual scans 1510 can be increased withingrid region 1512-2. These adjustments can be made as part of a closedloop control system. While only five individual scans 1510 are shownwithin each region this is done for exemplary purposes only and theactual number of individual scans within a grid region 1512 can besubstantially higher. For example, where the melt zone generated by theenergy source is about 80 microns wide it could take about 60 individualscans 1510 in order for all the powder within a 5 mm square grid region1512 to fall within the melt zone.

FIG. 15F shows an edge region of workpiece 1502 once the energy sourcefinishes traversing the pattern of tracks 1506. In some embodiments, theenergy source can continue to add energy to workpiece 1502 subsequent toa majority of the powder having been melted and resolidified. Forexample, contour scans 1514 can track along an outer periphery 1516 ofworkpiece 1502 in order to apply a surface finish to workpiece 1502. Itshould be appreciated that contour scans 1514 as depicted aresubstantially shorter than individual scans 1510. For this reason, gridregions 1518 can be substantially narrower than grid regions 1512. Itshould also be noted that grid regions 1518 are not purely rectangularin shape as in this case they follow the contour of the outer peripheryof workpiece 1502. Another instances that may result in scan lengthdifferences could be where a workpiece includes walls of varyingthickness. A variable thickness wall could result in scans lengthvarying within a single grid region. In such a case, an area of eachgrid region could be kept consistent by increasing the length of thegrid region while narrowing the width to conform to changes in thelength of individual scans.

FIG. 16 shows a closed loop control example showing feedback controlloop 1600 for establishing and maintaining feedback control of anadditive manufacturing operation. At block 1602 a baseline thermalenergy density for the next grid region across which the energy sourceis about to traverse is input into the control loop. This baselinethermal energy density reading can be established from modeling andsimulation programs and/or from previously run experimental/test runs.In some embodiments, this baseline thermal energy density data can beadjusted by energy density bias block 1604 which includes energy densityreadings for various grid regions recorded during preceding layers.Energy density bias block 1604 can include an adjustment to baselineenergy density block in instances where preceding layers received toomuch or too little energy. For example, where optical sensor readingsindicate a thermal energy density below nominal in one region of aworkpiece, energy density bias values can increase the value of thebaseline energy density for grid regions overlapping the grid regionswith below nominal thermal energy density readings. In this way, theenergy source is able to fuse additional powder that was not fully fusedduring the preceding layer or layers.

FIG. 16 also shows how the inputs from block 1602 and 1604 cooperativelycreate an energy density control signal that is received by controller1606. Controller 1606 is configured to receive the energy densitycontrol signal and generate energy source input parameters configured togenerate the desired thermal energy density within the current gridregion. Input parameters can include power, scan velocity, hatchspacing, scan direction and scan duration. The input parameters are thenreceived by energy source 1608 and any changes in the input parametersare adopted by energy source 1608 for the current grid region. Onceoptical sensors measure the scans of energy source 1608 making up thecurrent grid region, at block 1610 thermal energy density for thecurrent grid region is calculated and compared to the energy densitycontrol signal. If the two values are the same then no change to energydensity control signal is made based upon the optical sensor data.However, if the two values are different the difference is added orsubtracted from the energy density control signal for scans made in thenext grid region.

In some embodiments, grid regions for the current layer and allpreceding layers can be dynamically generated grid regions that areoriented in accordance with a path and scan length/width of scansperformed by the energy source. In this type of configuration bothbaseline energy density and energy density bias can both be based ondynamically generated grid regions. In other embodiments, grid regionsfor the current layer can be dynamically generated while energy densitybias data 1604 can be based upon energy density readings associated withstatic grid regions defined prior to the beginning of the additivemanufacturing operation, resulting in the static grid regions remainingfixed throughout the part and not varying in position, size or shape.The grids could be uniformly shaped and spaced when a Cartesian gridsystem is desired but could also take the form of grid regions making upa polar grid system. In other embodiments, the grid regions for thecurrent layer can be statically generated prior to the build operationbeing carried out and energy density bias data can also be staticallygenerated and share the same grid being used for the current layer.

In some embodiments, thermal emission density can be used in lieu ofthermal energy density with control loop 1600. Thermal emission densitycan refer to other factors in addition to thermal energy density. Forexample, thermal emission density can be a weighted average of multiplefeatures that include thermal energy density along with one or moreother features such as peak temperature, minimum temperature, heatingrate, cooling rate, average temperature, standard deviation from averagetemperature, and a rate of change of the average temperature over time.In other embodiments, one or more of the other features could be used tovalidate that the scans making up each of the grid regions are reachinga desired temperature, heating rate or cooling rate. In such anembodiment the validation features could be used as a flag to indicatethat input parameters for the energy source may need to be adjustedwithin a defined control window to achieve the desired temperature,heating rate or cooling rate. For example, if peak temperature withinthe grid region is too low power could be increased and/or scanningvelocity decreased. Although discussion of aforementioned control loop1600 related to various types of grid TED it should be noted that aperson with ordinary skill in the art would also understand that scanTED metrics could also be used in a similar loop configuration.

TED Analysis for Recoater Arm Short Feed

FIG. 17A shows a normal distribution of powder 1702 across a build plate1704. This depiction shows how the powder gets spread evenly without anyvariations in height. In contrast, FIG. 17B shows how when aninsufficient amount of powder 1702 is retrieved by a recoater arm, athickness of powder layer 1702 can vary greatly. Once the recoater armbegins to run out of powder 1702 a thickness of the layer of powder 1702gradually tapers off until portions of build plate 1704 are leftentirely bare of powder 1702. As errors of this sort can have quitenegative effects upon the overall quality of the part early detection ofthis type of phenomenon is important for accurate defect detection.

FIG. 17C shows a black and white photo of a build plate in which a shortfeed of powder 1702 resulted in only partial coverage or nine workpiecesarranged on a build plane. In particular, three of the workpieces on theright side of the photo are completely covered while three of theworkpieces on the left side are almost completely uncovered.

FIG. 17D shows how when an energy source scans across all nineworkpieces using the same input parameters, detected thermal energydensity is substantially different. Regions 1706 produce substantiallyhigher thermal energy density readings on account of the emissivity ofthe powder being substantially higher and the thermal conductivity ofthe build plate or solidified powder material being greater than theeffective thermal conductivity of the powder. The higher thermalconductivity reduces the amount of energy available for radiation backtoward optical sensors thereby reducing detected thermal radiation.Furthermore, the lower emissivity of the material itself also reducesthe amount of radiated thermal energy. F

Thermal Energy Density Vs Global Energy Density

Power provided by an energy source coming into the workpiece results inmelting of material making the workpiece, but that power can also bedissipated by several other heat and mass transfer processes during anadditive manufacturing process. The following equation describesdifferent processes that can absorb the power emitted where the energysource is a laser scanning across a powder bed:

P _(TOTAL LASER POWER) =P _(OPTICAL LOSSES AT THE LASER) +P_(ABSORPTION BY CHAMBER GAS) +P _(REFLECTION) +P_(PARTICLE AND PLUME INTERACTIONS) +P_(POWER NEEDED TO SUSTAIN MELT POOL) +P _(CONDUCTION LOSSES) +P_(RADIATION LOSSES) +P _(CONVECTION LOSSES) +P_(VAPORIZATION LOSSES)  Eq(13)

Optical losses at the laser refers to power losses due to imperfectionsin the optical system responsible for transmitting and focusing laserlight on the build plane. The imperfections result in absorption andreflection losses of the emitted laser within the optical system.Absorption by chamber gas refers to power loss due to gases within abuild chamber of the additive manufacturing system absorbing a smallfraction of the laser power. The impact of this power loss will dependon the absorptivity of the gas at the wavelength of the laser.Reflection losses refer to power lost due to light escaping the laseroptics that is never absorbed by the powder bed. Particle and plumeinteractions refer to interactions between the laser and a plume and/orparticles ejected during the deposition process. While power loss due tothese affects can be ameliorated though shielding gas being circulatedthrough the build chamber a small amount of power reduction generallycan't be completely avoided. Power needed to sustain melt pool refers tothe portion of the laser power absorbed by the working material formelting and ultimately superheating the powder to whatever temperaturethe melt pool ultimately achieves. Conduction losses refers to theportion of the power absorbed through conduction to solidified metalbelow the powder and the powder bed itself. In this way, the powder bedand solidified material making up the part will conduct heat away fromthe melt pool. This conductive transfer of thermal energy is thedominant form of energy loss from the melt pool. Radiation losses refersto that portion of the laser power that is emitted by the melt pool andmaterial surrounding the melt pool that is hot enough to emit thermalradiation. Convection losses refer to losses caused by the transfer ofthermal energy to gases circulating through the build chamber. Finally,vaporization losses refer to a small fraction of powder material thatwill vaporize under laser irradiation. The latent heat of vaporizationis very large, so this is a powerful cooling effect on the melt pool andcan be a non-negligible source of energy loss as the total laser beampower goes higher.

The thermal energy density (TED) metric is based on measurement ofoptical light that is a result of the radiation of light from the heatedregions, transmission of this light back through the optics, collectionof the light by the detector, and conversion of this light intoelectrical signals. The equation governing blackbody radiation over allpossible frequencies is given by the Stefan-Boltzmann equation shownbelow in Eq(14):

P _(RADIATED) =F _(HOT-OPTICS) ·ε·σ·A·(T _(HOT) ⁴ −T _(BACKGROUND)⁴)  Eq(14)

The variables from Eq(14) are shown below in Table (1).

TABLE 1 ε Emissivity is defined as the ratio of the energy radiated froma material's surface to that radiated from a blackbody (a perfectemitter) at the same temperature and wavelength and under the sameviewing conditions. P_(HOT-OPTICS) This is the view factor between theregions of the build plane hot enough to radiate and the optics of thelaser scan head. σ The Stefan - Boltzmann constant, 5.67 × 10⁻⁸ wattsper meter squared per degree kelvin to the fourth power A The area ofthe regions hot enough to radiate energy at any substantial amount. Notethat in general this will be LARGER than the area of the melt pool. Inother words, this term is NOT equivalent to the melt pool area. T_(HOT)The average temperature in degrees K of the region hot enough to radiatein any significant amount. T_(BACKGROUND) The temperature of the objector background medium to which the melt pool is radiating. This willgenerally be a significantly lower temperature, and therefore inpractice this term is of negligible magnitude.

There are additional intervening factors impacting the radiated lightbefore it is collected by the sensor and before it results in a voltagethat is used to calculate the TED metric. This is summarized below inEq(15):

V _(VOLTAGE USED BY TED) ={P _(RADIATED) −P _(VIEW FACTOR) −P_(OPTICAL LOSSES AT RADIATED WAVELENGTHS) −P_(SENSOR LOSS FACTOR)}*(SENSOR SCALING FACTOR)  Eq(15)

These various terms from Eq(15) are explained in Table (2) below.

TABLE (2) P_(RADIATED) The radiated power previously discussed as thepower radiated from the regions of the build plane (comprised of themelt pool and surrounding hot material) P_(VIEW FACTOR) This loss termtakes into account that not all of the radiated power leaving the hotregion will enter the optics of the scan head, and that there is ageometric view factor effect governing the amount of energy that can becollected. This could be numerically calculated by knowing all therelevant geometries and employing a ray tracing scheme.P_(OPTICAL LOSSES AT RADIATED WAVELENGTHS) This term accounts for lossesdue to the optics of the scan head and associated partially reflectiveand wavelength dependent mirrors that allow the light to go back throughthe scan head optics and to be collected at the sensorP_(SENSOR LOSS FACTOR) The sensor itself will have wavelength-dependentabsorption characteristics SENSOR SCALING FACTOR This is a numericalfactor for how photons received by photodiode are converted to electronsand result in a measureable voltage

Often in additive manufacturing, a figure of merit that is used is theglobal energy density (GED). This is a parameter that combines variousPROCESS INPUTS as shown below in Eq(16):

GED=(BEAM POWER)/{(TRAVEL SPEED)*(HATCH SPACING)}  Eq(16)

We notice that GED has units of energy per unit area:(JOULES/SEC)/{(CM/SEC)*(CM)}=JOULES/CM². However, it should be notedthat although GED may have the same unites as TED, GED and TED are NOTgenerally equivalent. As an example, TED is derived from the radiatedpower from the hot region divided by an area, whereas GED is a measureof input power. As described herein, TED relates to RESPONSE or PROCESSOUTPUT, whereas GED relates to a PROCESS INPUT. The inventors believethat, as a result, TED and GED are different measures from each other.In some embodiments, the area utilized in determining TED differs fromthe melt pool area. As a result, some embodiments do not have a directcorrelation between TED and the melt pool area. Beneficially, TED issensitive to a wide range of factors which directly impact the additivemanufacturing process.

Thermal Energy Density Based on Monitoring of Discrete Wavelengths ofLight

FIG. 18A shows an exemplary graph 1800 illustrating sensor readingstaken by a spectrometer during a laser-sintering additive manufacturingprocess using a powdered aluminum alloy. In some embodiments, thespectrometer can have a range of between 0 and 1500 nm covering both thevisible and near infrared spectra. A peak 1802 centered around 1064 nmcorresponds to a wavelength of an ytterbium doped laser that acts as theenergy source for the additive manufacturing process. Even when bafflesare installed on the laser, the magnitude of peak 1802 can beartificially increased due to light from the laser being reflected offof other surfaces in a build chamber prior to being sensed by thespectrometer. Due to the magnitude of peak 1802 the spectrometer becomessaturated with light from this wavelength, resulting in otherfrequencies of light that could otherwise be captured by thespectrometer being suppressed or in some cases completely obscured. Forexample, while peak 1804 might correspond to a blackbody radiationcurve, the amplitude of the signal is too low due for feature extractiondue to the saturation of the spectrometer by the laser light.

FIG. 18B shows exemplary graph 1850 which depicts a portion of sensorreadings taken by the spectrometer corresponding to the previouslyindicated peak 1804 after adding a band pass filter to the spectrometer,which blocks out frequencies surrounding peak 1802. For example, theband pass filter could be configured to remove frequencies of lighthaving frequencies of between 1000 and 1100 nm. Removing thesefrequencies from the spectrometer results in graph 1850 following thegeneral shape of a blackbody radiation curve with the exception ofcertain spectral feature peaks 1852. Spectral peaks 1852 result frommaterial properties of the powder undergoing laser irradiation. In someembodiments, these spectral peaks could occur due to the presence ofneutral atoms making up the powder, ionized powder as well as electronsfrom the ionization. These spectral peaks 1852 generally remain at fixedwavelengths. This allows wavelengths 1854 and 1856 to be selected atfrequencies offset from spectral peaks 1852. While a size of spectralpeaks 1852 may vary with temperature the range of wavelengths they coverstays substantially the same over a large range of temperatures. In someembodiments, it may also be desirable to establish a trendline followingthe shape of a black body curve and select wavelengths that areconsistently positioned upon the trend line defining the black bodycurve. This further helps prevent the placement of one of the selectedwavelengths 1854 or 1856 upon a spectral feature that could negativelyaffect the accuracy of temperature data derived from sensors monitoringwavelengths 1854 and 1856.

Optical sensors monitoring wavelengths 1854 and 1856 can be configuredto monitor a narrow range of wavelengths between 0.5 nm and about 10 nmthat are centered about the selected wavelengths. A size of the rangecan depend upon the application and characteristics of the powder andenergy source being used. In some embodiments, two different opticalsensors can be used to collect light emitted at wavelengths 1854 and1856. The optical sensors can take the form of photodetectors or morespecifically photodiodes with dielectric multi-layer wavelength notchfilters that limit the light reaching the photodiode to narrow ranges ofwavelengths centered about wavelengths 1854 and 1856, respectively.While wavelengths 1854 and 1856 are positioned upon opposing sides ofthe black body curve, it should be noted that the wavelengths can alsobe positioned on the same side of the curve as long as the wavelengthsdo not overlap.

A ratio of the intensity of light at wavelength 1854 to the intensity oflight at wavelength 1856 can be used to characterize changes orvariations in temperature on the build plane. These measurements aredriven by thermal radiation from the melt pool and a luminous plumeproximate the melt pool that is caused by vaporization of small portionsof the metal powder. A majority of the measurements come from theluminous plume as the luminous plume tends to mask the black bodyemissions from the melt pool. This configuration which monitors a verysmall range of light emitted from the build plane prevents a majority ofthe inaccuracies caused by broad spectrum monitoring. For example, thismethod of monitoring greatly reduces inaccuracies caused by laser lightreflecting off the walls of an additive manufacturing apparatus. Itshould be noted that the blackbody radiation curve can varysubstantially in wavelength depending upon the type of powdered metalbeing used. For example, while the graphs in FIGS. 18A and 18Bcorrespond to Aluminum, which has a blackbody radiation curve thatextends between 400 nm and 900 nm, a blackbody radiation curve fortitanium can be located between about 1400 and 1700 nm. For this reason,wavelengths 1854 and 1856 would be reselected when there are changes inmetal powder alloy. Reselection of wavelengths 1854 and 1856 could alsobe desirable when desired operating temperatures are changed. Anoperator could change operating temperatures for a part with the sametype of metal alloy when a different material grain structure isdesired.

FIG. 19A shows a graph 1900 illustrating a number of blackbody radiationcurves representative of various melt pool temperatures, ranging from3500K to 5500K and is a representation of the Planck radiation equation.In a case where a melt pool temperature of 4000K is desired to bemaintained throughout an additive manufacturing operation, wavelengthsof light 1902 and 1904 can be selected on either side of a peak ofblackbody radiation curve 1906. A ratio of an energy density recorded bya first sensor monitoring wavelengths of light 1902 to an energy densityrecorded by a second sensor monitoring the wavelengths of light 1904 canbe used to characterize temperature and heat within an additivemanufacturing system. When the ratio of these energy densities starts toincrease, a temperature of the melt pool can be determined to beincreasing. Similarly, a reduction in the value of the ratio decreasesis indicative of decreases in build plane/melt pool temperatures. FIG.19A show how a ratio of the intensities can be about 1.4 for blackbodyradiation curve 1906 while a ratio of the intensities can be about 1.5for blackbody radiation curve 1908 and a ratio of the energy densitiescan be about 2.7 for blackbody radiation curve 1910. In someembodiments, this energy density ratio can be used in lieu of rawintensity/energy density readings when calculating thermal energydensity in order to more accurately characterize an additivemanufacturing operation.

In addition to providing a useful feature that scales predictably withchanges in melt pool temperature, the relative intensities of light canalso be used to directly measure temperature. While the Stefan-Boltzmanlaw, see Eq(14) above, describes a relationship between power andtemperature, Planck's law describes the spectral density ofelectromagnetic radiation emitted by a blackbody in thermal equilibriumat a given temperature T. The law is named after Max Planck, whoproposed it in 1900. The law can be adapted to solve for temperature asshown below in Eq(17):

$\begin{matrix}{T = \frac{{hc}\text{/}{k_{B}\left( {{1\text{/}\lambda_{2}} - {1\text{/}\lambda_{1}}} \right)}}{{\ln \left( {{PD}_{\lambda_{1}}\text{/}{PD}_{\lambda_{2}}} \right)} + {5{\ln \left( {\lambda_{1}\text{/}\lambda_{2}} \right)}}}} & {{Eq}(17)}\end{matrix}$

In Eq(17), T corresponds to temperature of the melt pool, h is thePlanck constant (6.62×10⁻³⁴ J s), c is the speed of light in a vacuum,k_(B) is the Boltzmann constant (1.38×10⁻²³ J/K), λ₁ and λ₂ are twodiscrete wavelengths of light and PD_(λ) ₁ and PD_(λ) ₂ are intensitiesof light at the two respective wavelengths. This equation allows acalibrated temperature to be determined from the readings taken by twosensors sampling two discrete wavelengths of light.

FIG. 19B shows a graph 1950 and how a % change in power output effectsthe natural log of the ratio of the intensities sensed at the first andsecond wavelengths. Graph 1950 shows how the percent change in power isdirectly proportional to the natural log of the intensities of lightsensed at the first and second wavelengths. This consistent correlationallows for lookup tables to be developed that accurately track changesin energy input on the powder bed. In this way, any substantialvariations in this logarithmic intensity ratio can correspond toundesired problems in the build process.

FIG. 20A shows an exemplary additive manufacturing system that isequipped with three optical sensors in which two of the optical sensorsmonitor discrete wavelengths of light to characterize temperaturevariations in real-time occurring on a build plane and the third opticalsensor is configured to measure thermal energy density as describedabove relative to FIGS. 18A-19B. The thermal energy density is sensitiveto changes in process parameters such as, for example, energy sourcepower, energy source speed, and hatch spacing. The additivemanufacturing system of FIG. 20A uses a laser 2000 as the energy source.The laser 2000 emits a laser beam 2001 which passes through a partiallyreflective mirror 2002 and enters a scanning and focusing system 2003which then projects the beam to a region 2004 on build plane 2005. Insome embodiments, build plane 2005 is a powder bed. Optical energy 2006is emitted from region 2004 on account of high material temperatures andemissivity properties of the materials receiving being irradiated bylaser beam 2001.

In some embodiments, the scanning and focusing system 2003 can beconfigured to collect some of the optical energy 2006 emitted fromregion 2004. In some embodiments, a melt pool and luminous plume cancooperatively emit blackbody radiation from within region 2004. The meltpool is the result of powdered metal liquefying due to the energyimparted by laser beam 2001 and is responsible for the emission of amajority of the optical energy 2006 being reflected back toward focusingsystem 2003. The luminous plume results from vaporization of portions ofthe powdered metal. The partially reflective mirror 2002 can reflect amajority of optical energy 2006 received by focusing system 2003. Thisreflected energy is indicated on FIG. 20 as optical energy 2007. Theoptical energy 2007 may be interrogated by on-axis optical sensors2009-1 and 2009-2. Each of the on-axis optical sensors 2009 receive aportion of optical energy 2007 through mirrors 2008-1 and 2008-2. Insome embodiments, mirrors 2008 can be configured to reflect onlywavelengths λ₁ and λ₂, respectively. In some embodiments, opticalsensors 2009-1 and 2009-2 receive a total of 80-90% of the lightreflected through the optics train. Optical sensors 2009-1 and 2009-2can also include notch filters that are configured to block any lightoutside of respective wavelengths λ₁ and λ₂. Third optical sensor 2009-3can be configured to receive light from partially reflective mirror2002. As depicted, an additional mounting point could be included thatallows for installation of third optical sensor 2009-3. In someembodiments, optical sensors 2009-1 and 2009-2 can be covered by notchfilters while third optical sensor 2009-3 can be configured to measure amuch larger range of wavelengths. In some embodiments, optical sensor2009-1 or 2009-2 can be replaced with a spectrometer configured toperform an initial characterization of a blackbody radiation curveassociated with a batch of powder being used to perform an additivemanufacturing process. This characterization can then be used todetermine how the wavelength filters of optical sensors 2009-1 and2009-2 are configured to be offset and avoid any spectral peaksassociated with the black body curve characterized by the spectrometer.This characterization is performed prior to a full additivemanufacturing operation being carried out.

It should be noted that the collected optical energy 2007 may not havethe same spectral content as the optical energy 2006 emitted from thebeam interaction region 2004 because the optical energy 2007 hassuffered some attenuation after going through multiple optical elementssuch as partially reflective mirror 2002, scanning and focusing system2003, and one or more of partially reflective mirrors 2008. Theseoptical elements may each have their own transmission and absorptioncharacteristics resulting in varying amounts of attenuation that thuslimit certain portions of the spectrum of energy radiated from the beaminteraction region 2004. The data generated by on-axis optical sensors2009 may correspond to an amount of energy imparted on the workplatform. This allows the notch feature wavelengths to be selected toavoid frequencies that are overly attenuated by absorptioncharacteristics of the optical elements.

Examples of on-axis optical sensors 2009 include but are not limited tophoto to electrical signal transducers (i.e. photodetectors) such aspyrometers and photodiodes. The optical sensors can also includespectrometers, and low or high speed cameras that operate in thevisible, ultraviolet, or the infrared frequency spectrum. The on-axisoptical sensors 2009 are in a frame of reference which moves with thebeam, i.e., they see all regions that are touched by the laser beam andare able to collect optical energy 2007 from all regions of the buildplane 2005 touched as the laser beam 2001 scans across build plane 2005.Because the optical energy 2006 collected by the scanning and focusingsystem 2003 travels a path that is near parallel to the laser beam,sensors 2009 can be considered on-axis sensors.

In some embodiments, the additive manufacturing system can includeoff-axis sensors that are in a stationary frame of reference withrespect to the laser beam 2001. Additionally, there could be contactsensors on a recoater arm configured to spread metallic powders acrossbuild plane 2005. These sensors could be accelerometers, vibrationsensors, etc. Lastly, there could be other types of sensors such asthermocouples to measure macro thermal fields or could include acousticemission sensors which could detect cracking and other metallurgicalphenomena occurring in the deposit as it is being built.

In some embodiments, a computer 2016, including a processor 2018,computer readable medium 2020, and an I/O interface 2022, is providedand coupled to suitable system components of the additive manufacturingsystem in order to collect data from the various sensors. Data receivedby the computer 2016 can include in-process raw sensor data and/orreduced order sensor data. The processor 2018 can use in-process rawsensor data and/or reduced order sensor data to determine laser 2000power and control information, including coordinates in relation to thebuild plane 2005. In other embodiments, the computer 2016, including theprocessor 2018, computer readable medium 2020, and an I/O interface2022, can provide for control of the various system components. Thecomputer 2016 can send, receive, and monitor control informationassociated with the laser 2000, the build plane 2005, and otherassociated components and sensors.

The processor 2018 can be used to perform calculations using the datacollected by the various sensors to generate in-process quality metrics.In some embodiments, data generated by on-axis optical sensors 2009 canbe used to determine thermal energy density during the build process.Control information associated with movement of the energy source acrossthe build plane can be received by the processor. The processor can thenuse the control information to correlate data from on-axis opticalsensor(s) 109 and/or off-axis optical sensor(s) 110 with a correspondinglocation. This correlated data can then be combined to calculate thermalenergy density. In some embodiments, the thermal energy density and/orother metrics can be used by processor 2018 to generate control signalsfor process parameters, for example, laser power, laser speed, hatchspacing, and other process parameters in response to the thermal energydensity or other metrics falling outside of desired ranges. In this way,a problem that might otherwise ruin a production part can beameliorated. In embodiments where multiple parts are being generated atonce, prompt corrections to the process parameters in response tometrics falling outside desired ranges can prevent adjacent parts fromreceiving too much or too little energy from the energy source.

In some embodiments, the I/O interface 2022 can be configured totransmit data collected to a remote location. The I/O interface 2022 canbe configured to receive data from a remote location. The data receivedcan include baseline datasets, historical data, post-process inspectiondata, and classifier data. The remote computing system can calculatein-process quality metrics using the data transmitted by the additivemanufacturing system. The remote computing system can transmitinformation to the I/O interface 122 in response to particularin-process quality metrics. It should be noted that the sensorsdescribed in conjunction with FIG. 20 can be used in the described waysto characterize performance of any additive manufacturing processinvolving sequential material build up.

While the embodiments described herein have used data generated byoptical sensors to determine the thermal energy density, the embodimentsdescribed herein may be implemented using data generated by sensors thatmeasure other manifestations of in-process physical variables. Sensorsthat measure manifestations of in-process physical variables include,for example, force and vibration sensors, contact thermal sensors,non-contact thermal sensors, ultrasonic sensors, and eddy currentsensors. It will be apparent to one of ordinary skill in the art thatmany modifications and variations are possible in view of the aboveteachings.

FIG. 20B shows a similar configuration to FIG. 20A with the exceptionthat sensor assembly 2010 can be attached to the optics of laser 2000 byfiber optic cable 2012. This allows sensor assembly 2010 to be decoupledfrom the additive manufacturing device. In some embodiments, fiber opticcable could include a splitter that replaced optics 2008 allowing eachof optical sensors 2009-1, 2009-2 and 2009-3 to be supplied by aseparate optical cable. The splitter could also be configured toallocate appropriate portions of light 2007 to each of the opticalsensors. In this way, the sensors could be further split up. Sensorassembly 2010 would still be in electrical communication with computer2016 allowing for the various types of control described above to befully implemented regardless of the position of sensor assembly 2010.

FIG. 21 shows a block diagram illustrating a method for measuringoptical emissions during an additive manufacturing process. At 2102, aspectrometer is used to collect radiation from the melt pool during theadditive manufacturing process. Optical emissions from an energy sourceused to generate the melt pool will often saturate readings taken by thespectrometer. At 2104, a range of wavelengths corresponding to theoptical emissions made by the energy source and recorded by thespectrometer will be identified. In some embodiments, the range will bedetermined by including any wavelengths where the measured intensityexceeds a predetermined intensity threshold. After identifying thisrange of wavelengths, a band pass filter can be added to thespectrometer that prevents optical emissions from the energy source frombeing picked up or sensed by the spectrometer. Once the band pass filteris added to the spectrometer new readings can be taken by thespectrometer. Now that the spectrometer is no longer saturated withoptical emissions from the energy source, wavelengths across which ablackbody radiation curve extend can be identified. At 2106, thespectrometer data corresponding to the identified blackbody radiationcurve can be isolated. At 2108, spectral characteristics within theidentified blackbody radiation curves can be identified. These spectralcharacteristics can take the form of discontinuities such as steep peaksor troughs that vary greatly from the shape of the blackbody curve andare caused by various material properties of the metal powder. Atoptional step 2110, a wavelength corresponding to a peak of theblackbody radiation curve for a typical operating temperature isidentified. Depending upon the blackbody curve a trend line may begenerated in order to help identify the peak. Identification of theblack body peak at a particular operating temperature may be helpfulwhen an operator wishes to select wavelength pairs positioned onopposite sides of the blackbody curve.

FIG. 21 includes 2112, which describes the selection of wavelength pairsthat avoid spectral characteristics and/or spectral peaks associatedwith the black body radiation curve. Selected wavelength pairs can takethe form of a 10 nm range of wavelengths. In embodiments where it isdesired to position the wavelength pairs on opposing sides of theblackbody curve, a first wavelength of the wavelength pair has awavelength shorter than a wavelength corresponding to the peak of theblackbody curve and a second wavelength of the wavelength pair is longerthan the wavelength corresponding to the peak of the blackbody curve.While single wavelengths can be used to identify each wavelength of thewavelength pairs these pairs will typically take the form of a narrowrange of wavelengths having a width between 0.5 nm and 10 nm. At 2114,two photodetectors that can take the form of photodiodes are configuredwith notch filters that limit the sensing of the optical sensors to thewavelengths of a corresponding one of the wavelength pairs. At 2116, thephotodiodes can be calibrated and/or balanced so that a response profileof each is equivalent. Calibration can include measuring the performanceof various test operations to confirm the ratios of the intensities ofthe sensor readings taken by the two sensors are in line with expectedvalues. At 2118, the two sensors are configured to monitor andcharacterize an additive manufacturing process in real-time. A processorresponsible for making settings changes to the additive manufacturingprocess can be configured to adjust those settings in response tounexpected changes in the ratio of the intensities from the two opticalsensors. For example, the ratio of the intensities or the natural log ofthe ratio of the intensities can be used as a tracking metric for aclosed loop feedback control system, similar to the system shown in FIG.16. It should be noted that the dual wavelength characterizationdiscussed in the description accompanying FIGS. 18A-21 can be applied toany of the aggregation schemes described in the other portions of thisapplication.

Furthermore, the calibration scheme described in FIG. 21 will generallybe performed anytime there is a change in the type or character ofpowder being used. Even powder from the same manufacturer may vary overtime resulting in receiving a powder that varies slightly incomposition. Variations in composition can result in additional spectralfeatures/peaks that could result in one or both of the pair ofwavelengths needing to be shifted in order to produce accuratetemperature data.

The various aspects, embodiments, implementations or features of thedescribed embodiments can be used separately or in any combination.Various aspects of the described embodiments can be implemented bysoftware, hardware or a combination of hardware and software. Thedescribed embodiments can also be embodied as computer readable code ona computer readable medium for controlling manufacturing operations oras computer readable code on a computer readable medium for controllinga manufacturing line. The computer readable medium is any data storagedevice that can store data which can thereafter be read by a computersystem. Examples of the computer readable medium include read-onlymemory, random-access memory, CD-ROMs, HDDs, DVDs, magnetic tape, andoptical data storage devices. The computer readable medium can also bedistributed over network-coupled computer systems so that the computerreadable code is stored and executed in a distributed fashion.

The foregoing description, for purposes of explanation, used specificnomenclature to provide a thorough understanding of the describedembodiments. However, it will be apparent to one skilled in the art thatthe specific details are not required in order to practice the describedembodiments. Thus, the foregoing descriptions of specific embodimentsare presented for purposes of illustration and description. They are notintended to be exhaustive or to limit the described embodiments to theprecise forms disclosed. It will be apparent to one of ordinary skill inthe art that many modifications and variations are possible in view ofthe above teachings.

What is claimed is:
 1. An additive manufacturing method, comprising:identifying spectral peaks associated with a batch of powder; selectinga first wavelength and a second wavelength spaced apart from the firstwavelength, the first wavelength and the second wavelength being offsetfrom the identified spectral peaks; generating a plurality of scans ofan energy source across a layer of the batch of powder disposed upon abuild plane during an additive manufacturing operation; measuring anamount of energy radiated from the build plane at the first wavelength;measuring an amount of energy radiated from the build plane at thesecond wavelength; determining variations in temperature of an area ofthe build plane traversed by the plurality of scans based upon a ratioof energy radiated at the first wavelength to energy radiated at thesecond wavelength; determining that the variations in temperature areoutside a threshold range of values; and thereafter, adjustingsubsequent scans of the energy source across or proximate the area ofthe build plane.
 2. The additive manufacturing method of claim 1 whereinthe amount of energy radiated from the build plane at the firstwavelength is measured by an optical sensor monitoring wavelengthswithin 5 nm of the first wavelength.
 3. The additive manufacturingmethod of claim 2, wherein the optical sensor comprises at least one ofa photodiode, a pyrometer, or an imaging sensor.
 4. The additivemanufacturing method of claim 1 further comprising determining the areaof the build plane traversed by: determining a start point of a firstscan of the plurality of scans; determining an end point of the firstscan; and determining a length of the first scan by calculating adistance between the start point and the end point.
 5. The additivemanufacturing method of claim 1, further comprising: transmitting acontrol signal associated with a process parameter when the variationsin temperature are determined to be outside of the threshold range ofvalues.
 6. The additive manufacturing method of claim 1 wherein theenergy source corresponds to at least one of a laser or an electronbeam.
 7. The additive manufacturing method of claim 1 furthercomprising: mapping the thermal energy density to locations within apart being formed by the additive manufacturing operation by: receivingenergy source drive signal data indicating a path of the energy sourceacross the build plane; and determining a location of each of theplurality of scans using the energy source drive signal data.
 8. Theadditive manufacturing method of claim 1 further comprising: receivingposition data associated with the energy source.
 9. The additivemanufacturing method of claim 1, further comprising: receiving energysource drive signal data, wherein the energy source drive signal dataindicates when the energy source is turned on and when the energy sourceis turned off.
 10. An additive manufacturing method, comprising:identifying spectral peaks associated with a batch of powder; selectinga first wavelength and a second wavelength spaced apart from the firstwavelength, the first wavelength and the second wavelength being offsetfrom the identified spectral peaks; generating a plurality of scans ofan energy source across a layer of the batch of powder on a build plane;generating sensor readings during each of the plurality of scans usingan optical sensing system that monitors the first wavelength and thesecond wavelength; determining variations in temperature across thebuild plane during the plurality of scans using a ratio of the sensorreadings collected at the first wavelength to the sensor readingscollected at the second wavelength; determining when the variations intemperature are outside a threshold range of values; and thereafter,adjusting an output of the energy source.
 11. The additive manufacturingmethod of claim 10, wherein the ratio of sensor readings from the firstand second wavelengths is used to determine an absolute temperature of aportion of the layer of the batch of powder.
 12. The additivemanufacturing method of claim 10, wherein the energy source is a laserconfigured to output light having a laser wavelength and wherein thefirst and second wavelengths do not overlap with the laser wavelength.13. The additive manufacturing method of claim 10, determining a gridregion including the plurality of scans comprises: receiving energysource drive signal data indicating a path of the energy source acrossthe build plane; and defining a location, shape and size of the gridregion based upon the energy source drive signal data.
 14. The additivemanufacturing method of claim 13, wherein the energy source drive signaldata includes a distance between two or more scans of the plurality ofscans.
 15. An additive manufacturing method, comprising: identifyingspectral peaks associated with a batch of powder; selecting a firstwavelength and a second wavelength spaced apart from the firstwavelength, the first wavelength and the second wavelength being offsetfrom the identified spectral peaks; generating a plurality of scans ofan energy source across a layer of powder on a build plane; generatingsensor readings during each of the plurality of scans using an opticalsensing system that monitors the first and second wavelengths during theplurality of scans; for each of the plurality of scans, mapping portionsof each of the sensor readings to a respective one of a plurality ofregions of the build plane; for each of the plurality of regions:characterizing temperature variations within the region based on a ratioof the sensor readings taken at the first wavelength and the sensorreadings taken at the second wavelength; determining that thetemperature variations associated with one or more of the plurality ofregions are outside a threshold range of values; and thereafter,adjusting an output of the energy source.
 16. The additive manufacturingmethod as recited in claim 15, wherein the first wavelength is largerthan a wavelength associated with a peak of a blackbody radiation curveassociated with the batch of powder for an operating temperatureassociated with the additive manufacturing method and the secondwavelength is smaller than the wavelength associated with the peak ofthe blackbody radiation curve.
 17. The additive manufacturing method ofclaim 16, wherein a ratio of an intensity of the first wavelength to anintensity of the second wavelength increases with a temperature of amelt pool generated by the energy source.
 18. The additive manufacturingmethod of claim 15 wherein the plurality of regions are arranged in agrid across the build plane.
 19. The additive manufacturing method ofclaim 18, wherein the build plane is characterized by an area equal toan area of the grid.
 20. The additive manufacturing method of claim 18,wherein the regions are distributed evenly across the build plane.